code: def testDefaultIsSameAsNormal(self):
      b = 10
      scale = rng.rand(b) + 0.5
      loc = rng.randn(b)
      norm = normal.Normal(loc=loc, scale=scale, validate_args=True)
      sasnorm = sinh_arcsinh.SinhArcsinh(
          loc=loc, scale=scale, validate_args=True
      )
  
      x = rng.randn(5, b)
      norm_pdf, sasnorm_pdf = self.evaluate(
          [norm.prob(x), sasnorm.prob(x)]
      )
      self.assertAllClose(norm_pdf, sasnorm_pdf)
  
      norm_samps, sasnorm_samps = self.evaluate(
          [
              norm.sample(10000, seed=test_util.test_seed()),
              sasnorm.sample(10000, seed=test_util.test_seed()),
          ]
      )
      self.assertAllClose(loc, sasnorm_samps.mean(axis=0), atol=0.1)
      self.assertAllClose(
          norm_samps.mean(axis=0), sasnorm_samps.mean(axis=0), atol=0.1
      )
      self.assertAllClose(
          norm_samps.std(axis=0), sasnorm_samps.std(axis=0), atol=0.1
      )
  
file_path: probability/tensorflow_probability/python/distributions/sinh_arcsinh_test.py
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import laplace
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sinh_arcsinh
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the SinhArcsinhTest obj's underlying member method code with example usage: obj.testDefaultIsSameAsNormal() without return types